Brain-Inspired Computing under the Era of Large Model and Generative AI: Theory, Algorithms, and Applications

In recent years, there has been a remarkable increase in the size and complexity of machine learning models, driven by advances in hardware capabilities, algorithmic innovations, and the availability of vast amounts of data. These large models, often comprising billions or even trillions of parameters, have demonstrated superior performance across various tasks such as natural language understanding, computer vision, and speech recognition. Generative AI, on the other hand, focuses on the creation of new data instances that resemble real-world examples. Brain-inspired computing is an interdisciplinary field that combines knowledge from neuroscience, computer science, and engineering, among other disciplines, to develop new computational methods based on the principles of the human brain to solve various complex problems. In theory, brain-inspired computing seeks to draw knowledge from neuroscience to understand how the brain processes information. In algorithms, brain-inspired computing aims to develop algorithms that mimic the workings of the nervous system. In terms of applications, brain-inspired computing is widely used in various fields.



Therefore, this special issue focuses on the synthesis of brain-inspired computing methodologies within the evolving landscape of large models and generative artificial intelligence (AI). Within this context, brain-inspired computing offers insights into efficient information processing, adaptive learning, and robust decision-making in large models. We have witnessed that large models and generative AI provide a novel paradigm for brain-inspired computing. With the development of large model and generative AI, we believe that they can provide new perspectives, theories, and algorithms to the challenging problems of brain-inspired computing, and brain-inspired computing can also make great contributions to the development of large model and generative AI. Therefore, this special issue aims at reporting the latest development on brain-inspired computing under the era of large model and generative AI.



Topics of interest include but are not limited to:

1. Application of brain-inspired computing in advancing large models and generative AI

2. Application of large models and generative AI techniques in brain-inspired computing

3. Novel brain-inspired theory inspired by large models and generative AI

4. Novel algorithms for brain-inspired computing to realize generative AI

5. Integration of brain-inspired computing and large models/generative AI

6. Neuromorphic hardware implementation of large-scale neural networks

7. Optimization of large models for the deployment on low-power neuromorphic chip

8. Other novel brain-inspired computing models and algorithms